Urunov Ravsha

ISSN: 2776-0979, Volume 3, Issue 3, Mar., 2023

THE DIGITAL ECONOMY CONDITIONS MONTE-CARLO METHOD IN STATISTICAL STUDY OF SUSTAINABLE DEVELOPMENT OF REGIONS Urunov Ravshanbek Sadullaevich
Senior Lecturer, Department of Statistics and Econometrics, Tashkent Financial Institute

Annotation
The article proposes ways to improve the statistical assessment of sustainable development of regions. A model for identifying factors that strongly influence the sustainable development of regions has been developed and proposed.

Keywords: statistics, econometrics, Bayesian statistics, Monte Carlo model, sustainable development strategy, low income.

Introduction
Since the start of the pandemic, the International Monetary Fund has disbursed more than $ 118 billion to 87 countries, including support for 54 low-income countries, particularly African countries, 13 times the average. The worsening of the pandemic, coupled with inflationary concerns, could send a double blow to many emerging and emerging economies if developed economies normalize monetary policy sooner than expected and fiscal conditions tighten. With this in mind, the development of the digital economy in the republic and in their regions is very important, because everyone has seen in practice exactly how much the digital economy is needed during the pandemic. Therefore, the role of the digital economy inthe economies of countries is becoming increasingly important.
Today, the share of the digital economy in world GDP is 5.5%, and the share of digital technologies in various sectors of the economy and society is 22.5% of world GDP. This is mainly due to the contribution of developed countries.
It is clear that the development of the digital economy is playing a very important role in the overall development of the country. The Republic has also adopted a number of normative and legal acts in this regard, including the Decree of the President of the Republic of Uzbekistan dated October 5, 2020 PF-6079 " On approval of the Strategy" Digital Uzbekistan - 2030 "and measures for its effective implementation. " One of the main advantages of the development of the digital economy is the data presented in the form of digital statistics, which are a key factor in production in all areas of socio-economic activity, which increases the country's competitiveness,

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ISSN: 2776-0979, Volume 3, Issue 3, Mar., 2023

quality of life (low poverty) and economic growth. This is crucial to achieving the goal in creating direct econometric models.
In particular, it is not possible to create econometric models without data formed on the basis of statistical indicators. If the statistics are unreliable, it does not matter if the structured econometric models are put into practice. Therefore, it is important to know how and under what factors the economy of the country and its regions is developing on the basis of the development of the digital economy.
In statistical research, a number of methods of econometrics and statistics are used, from which conclusions are drawn. In this article, we propose a Monte Carlo model to reduce the level of poverty in the country and its regions with this in mind.
The main goal of the model is to reduce poverty in the country and its regions.
How can this be achieved? To this end, it would be expedient to work, first of all, to reduce the share of the poor among the population, but not to reduce the remaining population to the share of the poor. That is, it is necessary to reduce the level of poverty and increase confidence in the phenomenon of not reducing the remaining share of the poor, to determine the correlation between the indicators of sustainable development of regions and to determine whether they have a strong impact.
The Monte Carlo model is an analysis method used when the parameters are roughly known and information about the statistical distribution of these parameters is available. Numerous random parameter values are generated for analysis, calculated for each such value, and a statistical distribution is generated for the result.
This model emerged in the 1930s with Enrico Fermi in Italy, and then in Los Alamos in the 1940s with John von Neumann and Stanislav Ulam's suggestion that the relationship between stochastic processes and differential equations could be used "in reverse."
With the advent of the first new generation of electronic computers that could use numbers in mathematical models, interest in stochastic methods was renewed. Stanislav Ulam discussed his ideas with John von Neumann and then began to use the statistical selection method proposed by Ulam. Since the introduction of computers, this has been a great achievement for the model, and the Monte Carlo model has been applied to many problems where the stochastic approach is more effective than other mathematical methods. In 1949, Metropolis and Ulam published an article entitled The Monte Carlo Model, which gave rise to the term. Themathematicalinterpretation of thismodel is asfollows,supposeweneed to obtain theintegralof somefunction.We usean informalgeometricdescription of theintegral and understand it as the area under the graph of this function. In short, the traditional

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ISSN: 2776-0979, Volume 3, Issue 3, Mar., 2023

Monte Carlo integration algorithm is as follows: let’s say we want to calculate a simple integral and implement it as follows.

b

∫f(x)dx

a
Consider a random variable that is uniformly distributed in the integration range [a, b] . Then f (u) is also a random variable and its mathematical expectation is expressed as follows.

b

Ε f(u) = ∫f(x)φ(x)dx a

Here, φ(x)the distribution density of a random variable is u , equal _b−alocation [a, b] . Thus, the required integral is expressed as follows:
b

∫f(x)dx = (b − a)Ε f(u)

a
but the mathematical expectation f (u) of a random variable can be easily estimated and this random variable can be calculated and modeled as an average choice.

Thus, we distribute the N points evenly over [a, b] , calculating f(u_i)for each point u_i.
We calculate the next sample average _N ∑_i=1 f(u_i):. As a result, we obtain the approximate value of the integral:

∫b f(x)dx ≈ ^{b − a}∑f(u_i) ^a i−1
The accuracy of the assessment depends only on the number of points. This method
also has a geometric interpretation.
This model or method is very similar to the deterministic method described above. ^b−aas
In statistics and econometrics, indicators perform many functions. By simplifying, defining and summarizing indicators, data are prepared and used for each sector and made available to the public. They can be reworked or analyzed to make decisions and set important goals. Through them, it helps to measure progress towards sustainable development goals and meet its objectives. They are used to prevent economic, social, environmental and other problems.
Therefore, in this article, we proposed the Monte-Corlo method in the development of the most optimal options of econometric models of the relationship of sustainable

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ISSN: 2776-0979, Volume 3, Issue 3, Mar., 2023

development indicators, and discussed its theoretical aspects. One of the strengths of this model is that the factor that strongly influences the outcome sign can be used to further clarify the interdependence within the characters and its impact on the outcome. Therefore, it becomes clearer what indicators need to be focused on in the regions in order to achieve the goals set for sustainable development goals.